Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

The massive generation, extensive sharing, and deep exploitation of data in the big data era have raised unprecedented privacy threats. To address privacy concerns, various privacy paradigms have been proposed to achieve a good tradeoff between privacy and data utility. Particularly, differential privacy has been well accepted as one of the de facto standard for privacy preservation, and numerous schemes guaranteeing differential privacy have been proposed. Nonetheless, most of the existing works claiming a superior utility-privacy tradeoff only present specific methods, with distinct perspectives, and a complete comparative analysis and evaluation study has not been fully investigated. To this end, in this paper we review and investigate existing schemes on providing differential privacy from a broad and encompassing perspective to provide a comprehensive survey with respect to both the privacy guarantee and the effectiveness and efficiency in utility improvement. We categorize the existing schemes into distribution optimization, sensitivity calibration, transformation, decomposition, and correlations exploitation, based on their mechanisms in improving data utility. We also conduct some analysis and comparison of their various concepts and principles, focusing on improvements to data utility. Finally, we outline some challenges and provide future research directions.

With the increasing popularity of crowdsourcing services, high-dimensional crowdsourced data provides a wealth of knowledge. Nonetheless, unprecedented privacy threats to participants have emerged, due to complex correlations among multiple attributes and the vulnerabilities of untrusted crowdsourcing servers. Paradigms, based on differential privacy, have been proposed to release privacy-preserving datasets with statistical approximation. However, most existing schemes are limited when facing highly correlated attributes, and cannot prevent privacy threats from untrusted crowdsourcing servers. To address these issues, we propose two novel solutions, namely LoCop and DR LoCop, which guarantee local differential privacy based on the randomized response technique while synthesizing and releasing high-dimensional crowdsourced data with high data utility. Particularly, LoCop leverages copula theory to synthesize high-dimensional crowdsourced data via univariate marginal distribution and attribute dependence. Univariate marginal distribution is estimated by the Lasso-based regression algorithm from aggregated privacy-preserving bit strings. Dependencies among attributes are modeled as multivariate Gaussian copula. Based on LoCop, the enhanced solution DR LoCop not only takes advantage of C-vine copula to reflect conditional dependencies among high-dimensional attributes, but also achieves dimension reduction. Extensive experiments on real-world datasets demonstrate that our solutions substantially outperform the state-of-the-art techniques in terms of both data utility and computational overhead.

The privacy-preserving image retrieval technology permits users to retrieve outsourced images in a secure manner in cloud-assisted Internet of Things (IoT) environment. However, most of the existing schemes still have some blemishes, such as low performance, shared key and untraceable malicious users. To this end, we present a secure and traceable multikey image retrieval, named as STMIR. First, we design a novel privacy-preserving Mahalanobis distance comparison method (PPMDC) based on the learning with errors technology and Mahalanobis distance. And STMIR extracts image features utilizing convolutional neural network model to improve retrieval accuracy. Then, STMIR employs extracted image features, PPMDC and key conversion technology to achieve secure image retrieval that supports the multikey setting. Meanwhile, STMIR uses encrypted image watermarking technology to protect the content of images and track malicious users who redistribute images. Formal security analysis shows that STMIR can resist both ciphertext only attack and known background attack, and extensive experiments in real-world image data sets demonstrate effectiveness of STMIR in terms of retrieval accuracy, retrieval efficiency and traceability to malicious query users.

The industrial Internet information system has become the key to protecting the new generation of information infrastructure, and its data security storage and sharing are crucial for platform construction and alliance development. In response to security challenges, such as privacy leakage, data tampering, and lack of effective isolation in the current industrial Internet data storage and sharing process, this article proposes an industrial Internet data-sharing scheme based on multiclouds and multichains collaborative storage mode. The proposed scheme supports keyword search and utilizes certificateless threshold proxy re-encryption (PRE) technology. Our scheme allows industrial enterprises to securely store a large amount of sensitive data ciphertext in private clouds, while classifying and storing metadata ciphertext on different blockchains. A data protection algorithm that avoids private key escrow and supports ciphertext retrieval is constructed by combining certificateless PRE, threshold secret sharing, and searchable encryption techniques. In addition, a trust evaluation consensus algorithm is designed to select nodes with high trust as proxies in blockchain networks, solving the trust problem in traditional PRE schemes. Security analysis shows that our scheme can effectively resist collusion attacks and data tampering. Performance and simulation comparison analysis show that the main algorithms in our scheme have a lower time cost compared to the other typical schemes involved in comparison.